N1

N1

Row 1

Trend and Change Points

Summary

Row 2

Offence per 1,000 people (LSOA units)

Direction of Crime rates in Neighbourhoods (Sorted by most recent year)

Row 3

Key map feature

Further details

Row 4

Age-Sex Profile

Age-Ethnicity Profile

Age-Occupation Profile

Row 5

Description

Description

Description

N2

N2

Row 1

Trend and Change Points

Summary

Row 2

Offence per 1,000 people (LSOA units)

Direction of Crime rates in Neighbourhoods (Sorted by most recent year)

Row 3

Key map feature

Further details

Row 4

Age-Sex Profile

Age-Ethnicity Profile

Age-Occupation Profile

Row 5

Description

Description

Description

N3

N3

Row 1

Trend and Change Points

Summary

Row 2

Offence per 1,000 people (LSOA units)

Direction of Crime rates in Neighbourhoods (Sorted by most recent year)

Row 3

Key map feature

Further details

Row 4

Age-Sex Profile

Age-Ethnicity Profile

Age-Occupation Profile

Row 5

Description

Description

Description

N4

N4

Row 1

Trend and Change Points

Summary

Row 2

Offence per 1,000 people (LSOA units)

Direction of Crime rates in Neighbourhoods (Sorted by most recent year)

Row 3

Key map feature

Further details

Row 4

Age-Sex Profile

Age-Ethnicity Profile

Age-Occupation Profile

Row 5

Description

Description

Description

N5

N5

Row 1

Trend and Change Points

Summary

Row 2

Offence per 1,000 people (LSOA units)

Direction of Crime rates in Neighbourhoods (Sorted by most recent year)

Row 3

Key map feature

Further details

Row 4

Age-Sex Profile

Age-Ethnicity Profile

Age-Occupation Profile

Row 5

Description

Description

Description

---
title: "# "
output: 
  flexdashboard::flex_dashboard:
    #vertical_layout: fill
    orientation: rows
    self_contained: true
    source: embed
    social: menu
bibliography: ../references/references.bib
---

<style>                     
.navbar {
  background-color:green;
  border-color:black;
}
.navbar-brand {
color:white!important;
}
</style>   


```{r setup, include=FALSE}
packages = c("dplyr", "rgdal", "knitr",
             "sf", "viridis", "ggeasy",
             "tidyr","scales",
             "etasFLP","maptools","sp",
             "raster","leaflet","htmlwidgets",
             "htmltools", "ggplot2",
             "flexdashboard", "tidyverse",
             "leaflet.minicharts", "manipulateWidget")

## Now load or install&load all
package.check <- lapply(
  packages,
  FUN = function(x) {
    if (!require(x, character.only = TRUE)) {
      install.packages(x, dependencies = TRUE)
      library(x, character.only = TRUE)
    }
  }
)


# Setup knitr
knitr::opts_chunk$set(
  echo = FALSE, message = FALSE, warning = FALSE,
  # Save all figures in the output dir, you have to include them explicitly
  # with an <img> tag
  fig.path = "../output/img/", fig.show = "show"
)
```



```{r, include=FALSE}

library(crosstalk)
library(leaflet)
library(dplyr)
library(reactable)
library(kableExtra)
#library(formattable)

#generate all the necessary tables

#setting work directory
pasteo <- "R:/HLSS/Big Data Centre/Data/TRANSFER_FROM_SERVER_ROOM/20221014/GMP/AMO/"

#load all boundary shapefile

#load gm boundary
gm_bdry <- sf::read_sf(paste(pasteo, "boundaries/", "GMP_BOUNDARY_geo", ".shp", sep=""))

#load la boundary
gm_la <- sf::read_sf(paste(pasteo, "boundaries/", "GM_LA_geo", ".shp", sep=""))

#load wards boundary
gm_ward <- sf::read_sf(paste(pasteo, "boundaries/", "gm_ward_geo", ".shp", sep=""))

#load lsoa boundary
gm_lsoa <- sf::read_sf(paste(pasteo, "boundaries/", "GMP_LSOA_BOUNDARY_geo", ".shp", sep=""))

#load city centre boundary
gm_centre <- sf::read_sf(paste(pasteo, "boundaries/", "city_and_town_Centre_merged2", ".shp", sep=""))

```


```{r, include=FALSE}

breweries91 <- leaflet::breweries91

set.seed(1000)
gr <- sample(paste("N", 1:5, sep=""), 32, replace = TRUE)
  
breweries91_grouped <- cbind(breweries91, data.frame(id=gr))

```


```{r render subpages, include=FALSE}
#prepare data

#-----
#Get all unique offence group names
Offence_group <- unique(breweries91_grouped$id)

#order group
Offence_group <- Offence_group[order(Offence_group)]
#-----
# Create variable which stores all subpages outputs
out = NULL

#-----
# Set knitr options to allow duplicate labels (needed for the subpages)
options(knitr.duplicate.label = 'allow')

# Create temporary environment which we use for knitting subpage.RMD 
subpage_env <- new.env()


for (Og in Offence_group) {  #Og  <- "N1"
  # Filter data for product group 
  subpage_data <- breweries91_grouped[breweries91_grouped$id == Og,]

  # Assign filtered data and product group to subpage_env 
  assign("subpage_data", subpage_data, subpage_env)
  assign("Offence_group", Og, subpage_env)
  
  # Knit subpage.RMD using the subpage_env and add result to out vector
  out = c(out, knitr::knit_child('subpage.Rmd', envir = subpage_env))
}
```


`r paste(knitr::knit_child(text = out), collapse = '')`

<!-- **References** -->